Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data.

Dzida, Tomasz; Iqbal, Mudassar; Charapitsa, Iryna; Reid, George; Stunnenberg, Henk; Matarese, Filomena; Grote, Korbinian; Honkela, Antti; Rattray, Magnus

Dzida, Tomasz; Iqbal, Mudassar; Charapitsa, Iryna; Reid, George; Stunnenberg, Henk; Matarese, Filomena; Grote, Korbinian; Honkela, Antti; Rattray, Magnus.

Afiliación

Dzida T; Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.
Iqbal M; Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.
Charapitsa I; Chemical Biology Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany.
Reid G; Chemical Biology Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany.
Stunnenberg H; Department of Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, Netherlands.
Matarese F; Department of Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, Netherlands.
Grote K; Genomatix GmbH, Munich, Germany.
Honkela A; Helsinki Institute for InformationTechnology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland.
Rattray M; Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.

PeerJ ; 5: e3742, 2017.

Article en En | MEDLINE | ID: mdl-28970965

Palabras clave

Bayesian classifier; ChIP-Seq; Enhancer-promoter interaction; Estrogen receptor; Machine learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: PeerJ Año: 2017 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Estados Unidos

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